The Fundamental Problem of AI Agents

I’ve been using the AI Agent for less than a week, and I can say that their fundamental problem lies not in the agents’ architecture, but in the LLMs themselves. They don’t utilize the architectural potential of the Agent environment, they ignore skills, they don’t understand the documentation, and so on. The only solution at the current level of technology is to retrain the LLM models. Moreover, the LLM must be trained separately for each Agent environment. It must know the documentation perfectly, even when there is nothing in its context yet. And its behavior patterns must be tailored to utilize skills and the full potential of the Agent architecture. The problem here is not only that the LLM must be retrained separately for each Agent environment, but also that it must be retrained for each version of the environment. Will this mean that if we train the LLM for each environment and each version of the environment, Agent developers will be forced to increase the time between releases, otherwise the constant training of models will perpetually disrupt processes? An interesting question. What do you think, guys?